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Extended Abstract Introduction While the vast majority of climate scientists have concluded that the changes in the climate over the past few decades can be attributed to human activity [Doran and Zimmerman, 2009], there has been a degree of reluctance to attribute specific weather events to elevated CO2 concentrations. For example, Coumou and Rahmstorf [2012] have noted that there has been an exceptionally high incidence of extreme weather events over the past decade and that some of the events can be linked to climate change but nevertheless concede that particular events “cannot be directly attributed to global warming.” Moreover, the World Meteorological Organization has noted that the incidence of extreme weather events matches IPCC projections, but qualifies this conclusion by stating that “it is impossible to say that an individual weather or climate event was “caused” by climate change….” [World Meteorological Organization, 2011, p 15]. This claim of “attribution impossibility” is not a minor shortcoming; it leaves the causes of extreme events open to question, allowing climate skeptics to attribute the increased incidence of extreme events to so-called “natural variability.” In the United States, this has undermined the political consensus necessary to adopt robust, cost-effective policies to reduce CO2 emissions. This paper explores the relationship between CO2 and weather by addressing whether there is a causal relationship between the atmospheric concentration level of carbon dioxide and hourly temperature. The analysis begins by noting that traditional correlation analysis is not capable of addressing whether there is a causal relationship between CO2 and temperature because statistical methods alone cannot render results that establish or reject causality between two variables that are contemporaneously correlated. Nevertheless, it is possible to address the issue of causality by using more advanced statistical techniques. An Approach to Establishing Causality This paper addresses the issue of causality between CO2 and temperature by following the research of the Nobel Laureate Clive Granger [1969], who defined causality in terms of whether lagged values of a variable lead to more accurate predictions of some other variable. In his words, “The definition of causality …is based entirely on the predictability of the some series, say Xt. If some other series Yt, contains information in past terms that helps in the prediction of Xt … then Yt is said to cause Xt.” [Granger, 1969, p 430]. This study embraces this view of causality by examining whether lagged values of CO2 lead to more accurate forecasts of temperature. The specific approach adopted here is to exploit the diurnal nature of the variation in the hourly CO2 concentration levels by using the CO2 concentration level in hour t – 24 as an explanatory variable. This variable has a 0.96 correlation with the CO2 level in hour t but i

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A growing literature has affirmed with great confidence the feasibility of high penetration levels of wind and solar energy in electric generation under existing technology. This optimism is based in part on the belief that improved forecasting has effectively “solved” the challenge of intermittency. For example, the UK’s Royal Academy of Engineering has recently noted that wind energy’s capacity weighted forecast error of about five percent is evidence that the wind energy forecasts in Great Britain are highly accurate. This paper assesses this claim using data from Great Britain, 50Hertz in Germany, Amprion in Germany, the California ISO, the Bonneville Power Administration, the Midcontinent ISO, France, Western Demark, Eastern Denmark, and Belgium. The analysis proceeds by comparing the forecast accuracy of load, wind, and solar energy with the accuracy of the corresponding persistence forecasts for load, wind, and solar energy. The analysis indicates that while the load forecasts are generally more accurate than a persistence load forecast, the wind and solar energy forecasts are generally less accurate than the corresponding persistence forecasts for wind and solar energy. Specifically, with a persistence forecast as reference, the mean-square-error-skill-score (MSESS) of the load forecasts are generally positive while the MSESS of the wind or solar energy forecast are generally negative. Evidence is also presented that the forecast errors have a systematic component. Modelling of this systematic component can yield short-run solar and wind energy forecasts that are significantly more accurate. This does not resolve the challenge of intermittency but may mitigate matters.

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